Systems and Methods for Assessing the Marketability of a Product

ABSTRACT

Embodiments of the present invention generally relate to systems and methods for psycho-physiological mood mapping. More specifically, the present invention relates to systems and methods for monitoring various parameters such as, but not limited to, facial muscle activity, heart rate changes, skin conductance changes, electrical charges across scalp, eye tracking, and behavior analysis and analyzing the data of such parameters via a 3D mood map. This data analysis may be used for many purposes including, without limitation, assessing the marketability of a product.

BACKGROUND OF THE INVENTION

Embodiments of the present invention generally relate to systems andmethods for assessing the market readiness of a product. Morespecifically, the present invention relates to systems and methods forassessing the market readiness of a product via psycho-physiologicalmood mapping.

In the field of applied consumer neuroscience, it has been determinedthat people see, interpret and behave in the world via four (4) generalsteps: forming impressions; determining meaning and value; deliberatingand analyzing; and speaking and acting. That is, as we interact with theworld around us, we non-consciously take in information. As we findmeaning and importance in these inputs, we become consciously aware ofthem, deciding how we will react.

Products are experienced via sensory systems of sight, smell, taste,touch and sound (i.e. five (5) dimensionally). Each is an opportunityfor the product to communicate with the consumer. This experience formsimpressions in the brain that affect mood and arousal levels whilesetting a context for the product. By helping product developers tounderstand the consumer experience through the senses, we help themuncover new opportunities for product innovation.

Background information for measuring emotion may be found in thefollowing:

Bradley & Lang (1994). Measuring emotion: the Self-Assessment Manikinand the Semantic Differential, J Behav Ther Exp Psychiatry, 25(1):49-59;

Dan-Glauser, E. S., & Scherer, K. R. (2011). The Geneva affectivepicture database (GAPED): a new 730-picture database focusing on valenceand normative significance. Behavior Research Methods, 43(2), 468-477,and

Mehrabian & Russell (1974). The basic emotional impact of environments.Percept Mot Skills, 38(1):283-301.

The Positive and Negative Affect Schedule (PANAS) developed by Watson,Clark, and Tellegen (1988b).

BRIEF SUMMARY OF THE INVENTION

Briefly stated, in one aspect of the present invention, a system forassessing the market readiness of a product is provided. This systemincludes a computing device including a non-transitory computer readablemedium for gathering and assessing various data, comprising instructionsstored thereon, which when executed by a processor read data inputsrelated to one or more of the following: facial muscle activity, heartrate changes, skin conductance changes, electrical charges across scalp,eye tracking, and behavior analysis. This data may be utilized forpsycho-physiological mood mapping.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofpreferred embodiments of the invention, will be better understood whenread in conjunction with the appended drawings. For the purpose ofillustrating the invention, there is shown in the drawings embodimentswhich are presently preferred. It should be understood, however, thatthe invention is not limited to the precise arrangements andinstrumentalities shown. In the drawings:

FIG. 1 depicts one system for assessing the marketability of a productin accordance with one embodiment of the present invention;

FIG. 2 depicts a block diagram of an exemplary computing device for usewith the systems and methods described herein;

FIG. 3 depicts an exemplary two-dimensional mood map depicting arousalvs. pleasantness;

FIG. 4 depicts an exemplary three-dimensional mood map depicting arousalvs. pleasantness vs approach/avoidance;

FIG. 5 depicts one method of assessing the marketability of a product inaccordance with one embodiment of the present invention;

FIG. 6 depicts a table of exemplary data collected via the exemplarysystem 100 of FIG. 1;

FIG. 7 depicts an exemplary three-dimensional mood map prior to mappingof subject and benchmark data points in accordance with one embodimentof the invention;

FIG. 8A depicts an exemplary x-y-z map prior to mapping of an emotionalpoint in accordance with one embodiment of the invention;

FIG. 8B depicts an exemplary mood map including a mapped emotional pointin accordance with one embodiment of the invention;

FIG. 9A is a graphical representation of a neutral emotion in accordancewith one embodiment of the invention;

FIG. 9B is a graphical representation of a negative-aroused emotion inaccordance with one embodiment of the invention;

FIG. 9C is a graphical representation of a negative-calm emotion inaccordance with one embodiment of the invention;

FIG. 9D is a graphical representation of a positive-aroused emotion inaccordance with one embodiment of the invention; and

FIG. 9E is a graphical representation of a positive-calm emotion inaccordance with one embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain terminology may be used in the following description forconvenience only and is not limiting. The words “lower” and “upper” and“top” and “bottom” designate directions in the drawings to whichreference is made. The terminology includes the words above specificallymentioned, derivatives thereof and words of similar import.

Furthermore, the subject application references certain processes whichare presented as series of ordered steps. It should be understood thatthe steps described with respect to those processes are not to beunderstood as enumerated consecutive lists but could be performed invarious orders while still embodying the invention described herein.

Where a term is provided in the singular, the inventors also contemplateaspects of the invention described by the plural of that term. As usedin this specification and in the appended claims, the singular forms“a”, “an” and “the” include plural references unless the context clearlydictates otherwise, e.g., “a sensor” may include a plurality of sensors.Thus, for example, a reference to “a method” includes one or moremethods, and/or steps of the type described herein and/or which willbecome apparent to those persons skilled in the art upon reading thisdisclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs. Although any methods andmaterials similar or equivalent to those described herein can be used inthe practice or testing of the present invention, the preferred methods,constructs and materials are now described. All publications mentionedherein are incorporated herein by reference in their entirety. Wherethere are discrepancies in terms and definitions used in references thatare incorporated by reference, the terms used in this application shallhave the definitions given herein.

Disclosed herein are systems and methods for assessing the marketreadiness of a product. As used herein, “product” is not limited to aphysical product but can also include, for example, services,experiences, and the like. These systems and methods may includeassessing whether revisions are required for such product and suggestingwhich aspects of the product require revision, in some embodiments ofthe invention, the systems and methods utilize psycho-physiological moodmapping.

In one embodiment of the present invention, three psycho-physiologicalmeasures, namely, facial electromyography (“fEMhG”) heart ratevariability (“HRV”), and galvanic skin response (“GSR”) are monitoredand the results are mapped as vectors to create a three-dimensional moodmap, as such measures reflect emotional valance, motivation (ordominance or approach/withdrawal or attention to or from a stimulus),and arousal (e.g., arousal experienced in response to a stimulus),respectively. However, such measures may also be obtained in othermanners including, without limitation, via survey (e.g., using the SelfAssessment Manakin (“SAM”) by Bradley & Lang 1994) The theory behind thepsycho-physiological mood map is based in part on thePleasure-Arousal-Dominance (“PAD”) Theory of Emotion as outlined byMehrabian and Russell (1974). The basic emotional impact ofenvironments. Percept Mot Skills, 38(1):283-301.

However, more or less measures may be monitored and/or recordedincluding, without limitation, electroencephalogram (“EEG”) measurementof electrical charges across the scalp which may correlate tomotivation, eye tracking which may correlate to visual attention, andbehavior analysis such as facial, routines, facial muscle activity,heart rate changes, skin conductance changes, etc.

fEMG refers to an electromyography technique that measures muscleactivity by detecting and amplifying the tiny electrical impulses thatare generated by muscle fibers when they contract. It primarily focuseson two major muscle groups in the face, the corrugators supercilii groupwhich is associated with frowning and the zygomaticus major muscle groupwhich is associated with smiling. In some aspects, orbicularis oculi mayalso be monitored for muscle activity. In one aspect of the presentinvention, fFMG is used to measure emotional valence experienced inresponse to a stimulus (e.g., pleasure), however, alternate systemsand/or methods of measuring pleasure may be substituted withoutdeparting from the scope hereof.

HRV refers to the physiological phenomenon of variation in the timebetween heartbeats. It is measured by the variation in the beat-to-beatinterval. In one aspect of the present invention, HRV is used to measuredominance, however, alternate systems and/or methods of measuringdominance (or approach/withdrawal) may be substituted without departingfrom the scope hereof.

GSR refers to the property of the human body that causes continuousvariation in the electrical characteristics of the skin (e.g., changesin electrical conductance across the skin), which is also sometimesreferred to as electrodermal activity (“EDA”). The relationship betweenemotional arousal and sympathetic activity and these autonomicsympathetic changes alter sweat and blood flow, which in turn affectsGSR. In one aspect of the present invention, GSR is used to measurearousal in response to a stimulus, however, alternate systems and/ormethods of measuring arousal may be substituted without departing fromthe scope hereo.

EEG refers to a non-invasive method to record electrical activity of thebrain along the scalp. EEG measures voltage fluctuations resulting fromionic current within the neurons of the brain. In some aspects of thepresent invention, EEG is used to measure pleasure and/or dominance,however, alternate systems and/or methods of measuring pleasure and/ordominance may be substituted without departing from the scope hereof.

Eye tracking is the process of measuring either the point of the gaze(where one is looking) or the motion of the head. An eye tracker deviceis utilized to measure eye positions and movement. In some embodimentsof the present invention, eye tracking is utilized to determine the timeat which an emotional occurrence occurs, for example, if such emotionaloccurrence occurs in response to a particular behavior or visualstimulus.

In some embodiments of the present invention, the systems and methodsfor creating a psycho-physiological mood map includes an algorithm forcalculating a three-dimensional (“3D”) space on a mood map based on theweighted psycho-physiological measures. An example of one such mood mapis depicted in FIGS. 4, 7, and 8B

Referring now to FIG. 4, depicted is an exemplary 3D mood map. Thedepicted map displays measures of a moment, i.e., a position vector in a3D space indicating a discrete emotional experience.

Referring now to FIG. 8A, depicted is an exemplary x-y-z map prior tothe mapping of an emotional point in accordance with one embodiment ofthe present invention. The x-y-z map includes three planes: the x-yplane located between the x and y axes; the x-z plane located betweenthe x and z axes: and the y-z plane located between the y and z axes.The x-y plane may also be described using the equation z=0 since allpoints on that plane will have a value of zero for its z-value This areais the 2d emotional space Similarly, the x-z plane may be describedusing the equation y=0 since all points on that plane will have a valueof zero for its y value. Similarly, the y-z plane may be described usingthe equation x=0 since all points on that plane will have a value ofzero for its x value.

Turning next to FIG. 8B, depicted is an exemplary mood map including amapped emotional point P (a, b, c). The 2d emotional point N is locateddirectly below P on the x-y plane. Where P is the discrete mappedemotional 3D point, a represents emotional valence, b representsarousal, and c represents motivation, Units measured from point 0 aretermed emotional units for each plane, and these units are calculated bycalculating standard deviations in change scores from baselinephysiological measures.

The distance from (0,0,0), or neutral emotion, to the point P (a, b, c)is determined by the following equation; distance OP=√a2+b2+c2. Thisdistance is the emotional moment arm, or effect moment indicating thestrength of the emotional experience calculated in response to astimulus as measured in physiological emotional units.

Since emotions are not discrete emotional points, but rather take up a3D space, any measured discrete point is defined by the emotional spacein which it lies Emotional spaces are defined by measuring reactions topre-defined and validated emotional imagery (validated using othervalidated measures such as calculations validated with SAM and imagespre-emotionally defined by GAPED.

Examples of emotional spaces in accordance with one embodiment of theinvention are shown in FIGS. 9A though 9E. FIGS. 9A through 9E depictneutral emotions, negative-aroused (anger, fear), negative-calm(bored-sad), positive-aroused (glad, happy, joy), and positive-calm(relaxed, content), respectively. That is, as shown in FIG. 9A, neutralemotion occurs within the area of 1 standard deviation around (0,0,0).This point is also considered an emotional baseline.

As shown in FIG. 93, negative-aroused emotions such as anger and fearoccur within the negative valence and positive arousal quadrant. Angeris defined as positive z within this space, and fear is defined asnegative z within this space.

As depicted in FIG. 9C, negative-calm emotions such as sad and boredoccur within the negative valence and negative arousal quadrant Sad isdefined as positive z within this space, and bored is defined asnegative z within this space.

As shown in FIG. 9D, positive-aroused emotions such as glad, happy andjoy occur within the positive valence and positive arousal quadrant.Glad is defined as negative z within this space, happy is defined as aneutral z within this space, and joy is defined as positive z withinthis space.

Similarly, as shown in FIG. 9E, positive-calm emotions such as relaxedand content occur within the positive valence and negative arousalquadrant, Relaxed is defined as negative z within this space, andcontent is defined as positive z within this space

The mood maps created herein may then be applied for assessing consumeremotion response to products and product attributes. For example, bytracking physiological changes after exposure to a product or productattribute and then mapping those changes to an emotional response, thesystems and methods of the present invention are able to visualize orassess unconscious emotional consumer responses and reactions to aproduct.

In cases in which three measures (e.g., arousal, valence, andmotivation) need to be reduced to a single piece of data for the sake ofsimplicity, geometric formulas are applied to find the exact distancefrom a zero point (as depicted in FIG. 8B) to the measured point in the3D space. This may be done by using the distance formula:

d=sqrt((x ₂ −x ₁){circumflex over ( )}2+(y ₂ −y ₁){circumflex over( )}2)

Since SAM measures range on the integers 1 through 9, this approach maybe easily applied to the SAM information. First we calculate thedistance from the max point to zero using a formula such as:

d _(max to zero)=sqrt((x _(max)−0){circumflex over ( )}2+(y_(max)−0){circumflex over ( )}2)=sqrt((9−0){circumflex over( )}2+(9−0){circumflex over ( )}2)=−12.73.

In this case, the variables x_(max) and y_(max) are the maximum possiblevalues of 9 and 9.

Then, to find the distance from the max point to the measured point, thefollowing formula may be used:

d_(max to measured)=sqrt((x_(max)−x_(measured)){circumflex over( )}2+(y_(max)−y_(measured)){circumflex over ( )}2), wherein thevariables x_(measured) and y_(measured) are the measured values forvalence and arousal (e.g., a and b as discussed above and as depicted inFIG. 8B)

Finally, we can subtract d_(max to measured) from d_(max to zero) to getd_(measured to zero):

d _(measured to zero)=sqrt((x _(max)−0){circumflex over ( )}2+(y_(max)−0){circumflex over ( )}2)−sqrt((x _(max)-x_(measured)){circumflex over ( )}2+(y _(max) −y _(measured)){circumflexover ( )}2)

or

d _(measured to zero)=12.73−sqrt((9−x _(measured)){circumflex over( )}2+(9−y _(measured)){circumflex over ( )}2)

This gives us a collapsed representation of two dimensions as a singlevalue. To generate a collapsed representation of a 3^(rd) dimension, theprocess is repeated. This time, the distance that has just beencalculated is treated as the x value with a maximum range value of 12.73instead of 9. The y value comes from the measured value for motivation(e.g., variable c as mentioned above and as depicted in FIG. 7B), whichcontinues to have the maximum range of 9.

d _(max to zero)=sqrt((x _(max)−0){circumflex over ( )}2+(y_(max)−0){circumflex over ( )}2)=sqrt((12.73−0){circumflex over( )}2+(9−0){circumflex over ( )}2)=˜15.59,

which provides the following final formula:

d _(measured to zero)=15.59−sqrt((12.73−x _(measured)){circumflex over( )}2+(9−y _(measured)){circumflex over ( )}2)

Now the value of d_(measured to zero) represents the absolute value ofthe measured data point's distance from zero.

This same approach may be applied to biometric data. Instead of usingnine as a maximum and zero as a minimum, the range is defined by themaximum and minimum recorded by the electrophysiological equipment.Although not as clear cut as the SAM calculation, it offers the sameability to simplify in order to make sense of complex data. Thefollowing steps may be taken to determine physiological responses toaffective norms. First, as step 1, images are selected from a databasewith pre-scored affective ratings, One such suitable database is theGAPED, which can be found at

. These images are chosen to represent a wide range of emotionalcontent. They are prepared using stimulus presentation software such ase-Prime software as manufactured by Psychology Software Tools, Inc.(“e-Prime”) to allow the stimuli to be shown to testing subjects whomeet certain required criteria. Such stimulus presentation software istypically capable of showing a variety of types of stimuli including,without limitation, images and videos, Physiological data (e.g., fEMG,EDA, and EKG) is then measured for the subjects being present with thestimuli utilizing data measuring equipment as manufactured by BiopacSystems, Inc. (“Biopac”).

Second, as step 2, responses to the images shown are gathered fromsubjects via survey software pertaining to categorical descriptions ofemotion based on a Positive and Negative Affective Schedule such as theone found at

. Then, the results are prepared in a spreadsheet or other format(using, for example, Microsoft Excel) in order to facilitate comparisonof physiological response and categorical response. Each categoricalitem is then calculated to have three (3) coordinate values in spacebased on the three (3) physiological measures. In the depictedembodiment, these coordinate values are the change from baseline for thephysiological measures, namely, the x coordinate is the change frombaseline for HRV, the y value is the change from baseline for GSR, andthe z value is the change from baseline for fEMG. In this manner,affective norms as represented in a conceptual 3D space are calculated.

Third, as step 3, a relation of normative coordinates to new measureddata is determined, To do this, normative affective coordinates (i.e.,the area in 3D space in which categorical descriptions of emotion exist)are plotted via graphing software. A new stimulus is presented tosubjects, and the three (3) aspects of physiological data are acquiredagain from the subjects using, for example, Biopac equipment andAcqknowledge software. The coordinates of the response to the newstimulus are mapped within the 3D space, and their relative distances topre-existing categorical norms are determined. The distance between thenorms and the new measured stimulus are used to apply a categoricalaffective label to the new measured stimulus.

After steps 1 and 2 are performed a single time, a basis for theaffective norms is established. These steps can be repeated to providemore emotional landmarks for the affective norms if desired. Step 3 isrepeated for each product for which market readiness is being tested fora particular product. Step 3 applies the mood map established in steps 1and 2 to the data generated by exposure of the subjects to the productor product stimuli being tested in order to determine whether theemotional goals of the product are being met.

In another aspect of the present invention, the results will bevalidated by correlating the physiological measures to validated SAMmeasures of documented and emotionally assessed imagery for use incommercial consumer research. See Bradley M M, Lang P J (1994).Measuring emotion: the Self-Assessment Manikin and the SemanticDifferential. J Behav Ther Exp Psychiatry, 25(1):49-59

In one embodiment of the present invention, “engagement” is calculatedusing arousal (GSR) and emotional valence (fEMG) measurements. Forexample, in one embodiment of the present invention, engagement iscalculated as follows:

Engagement=√(ΔArousal²+ΔEmotion²)

The calculated engagement can then be utilized in conjunction with theDominance measure to determine a biometrics score as follows:

Biometrics Score=√(Engage²+Dominance²)

In the above exemplary embodiment, more weight is placed on the arousaland emotional valence measurements, however, alternate methods ofcalculating engagement and biometrics scores may be substituted withoutdeparting from the scope hereof.

Referring now to FIG. 5, depicted is a flowchart of one method ofassessing market readiness of a product in accordance with oneembodiment of the invention Assessing market readiness of a product, orconsumer testing of a product, may include, inter alia, testing consumerresponses to physical, virtual and concept consumer products,advertisements, communications and/or experiences. This process caninclude testing product attributes such as sensory (e.g., taste, touch,smell, sound, and visuals), communications, and associative descriptors.Consumer physiological responses for these attributes can be used todefine, differentiate and assess current and test products for thepurposes of benchmarking performance and product optimization, They canalso be used to assess the emotions generated in the consumer by theproduct, advertisement, communication or experience.

The systems and methods of the present invention determine the need forpotential product revisions by assessing whether a product meetsemotional targets, which may include further assessing which aspects ofthe product may require revision. For example, in the case of afragrance test, recommendations may be made as to how to adjust thefragrance to achieve an emotional goal such as joy or calm.

The systems and methods of the present invention determine or help todetermine whether to proceed to market with a potential product byassessing the emotional impact of the product, or the fit of the productrelative to the concept of the product. For example, in the case ofholistic product testing, a positive emotional mapping location wouldsuggest product cohesion with concept and, therefore, fitness forproceeding to market. Conversely, a negative emotional mapping locationwould suggest that the product is not aligned with the concept and isnot ready to proceed to market.

Steps 502 through 513 of method 500 may also be utilized to determine asingle data point for mapping each of one or more benchmark points on a3D mood map such as map 700

As depicted in FIG. 5, the method of assessing the market readiness of aproduct or service involves, inter alia, the following steps: 1)physiologically measuring the consumer product testing subjects whilethey are being exposed to the product or a product stimulus (e.g.,product, advertisements, etc.); 2) processing the data measured in step1; and 3) determining a single data point for mood comparisons andassessment. That is, in some embodiments, the product stimulus may bethe actual product. In other embodiments, the product stimulus may be inthe form of something that relates to the product but is not the actualproduct. Using the single data point or value, statistical comparisonsmay be made to assess product performance by comparing the new product'sperformance to a benchmark value. This benchmark value may include,without limitation, a benchmark value associated with a previouslytested product, a competitive product, or an idealized emotionalconcept.

Turning now to FIG. 2, depicted is an exemplary computing device 202 foruse with the systems and methods described herein. The depictedcomputing device is only one example of a suitable computing device andis not intended to suggest any limitation as to the scope of use orfunctionality. Numerous other general purpose or special purposecomputing system devices, environments, or configurations may be used,Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use include, but are not limitedto, personal computers (“PCs”), server computers, handheld or laptopdevices, multi-processor systems, microprocessor-based systems, networkPCs, minicomputers, mainframe computers, cell phones, tablets, embeddedsystems, distributed computing environments that include any of theabove systems or devices, and the like.

Computer-executable instructions such as program modules executed by acomputer may be used, Generally, program modules include routines,programs, objects, components, data structures, etc. which performparticular tasks or implement particular abstract data types.Distributed computing environments may be used where tasks are performedby remote processing devices that are linked through a communicationsnetwork or other data transmission medium. In a distributed computingenvironment, program modules and other data may be located in both localand remote computer storage media including memory storage devices.

In the depicted embodiment, computing device 202 includes at least oneprocessing unit 202 and at least one memory 204. Depending on the exactconfiguration and type of the computing device, memory 204 may bevolatile (such as random access memory (“RAM”)), non-volatile (such asread-only memory (“ROM”), flash memory, etc.), or some combination ofthe two. This most basic configuration is illustrated in FIG. 2 bydashed lines 206. In addition to that described herein, computingdevices 202 can be any web-enabled handheld device (e.g., cell phone,smart phone, or the like) or personal computer including those operatingvia Android™, Apple®, and/or Windows® mobile or non-mobile operatingsystems.

Computing device 202 may have additional features/functionality. Forexample, computing device 202 may include additional storage (removableand/or non-removable) including, but not limited to, magnetic or opticaldisks or tape, thumb drives, and external hard drives as applicable.Such additional storage is illustrated in FIG. 2 by removable storage208 and non-removable storage 210.

Computing device 202 typically includes or is provided with a variety ofcomputer-readable media. Computer-readable media can be any availablemedia that can be accessed by computing device 202 and includes bothvolatile and non-volatile media, removable and non-removable media, Byway of example, and not limitation, computer-readable media may comprisecomputer storage media and communication media.

Computer storage media includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Memory 204, removable storage 208, andnon-removable storage 210 are all examples of computer storage media.Computer storage media includes, but is not limited to, RAM, ROM,electrically erasable programmable read-only memory (“EEPROM”), flashmemory or other memory technology, CD-ROM, digital versatile disks(“DVD”) or other optical storage, magnetic cassettes, magnetic tape,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to store the desired information and which canbe accessed by computing device 202. Any such computer storage media maybe part of computing device 202 as applicable.

Computing device 202 may also contain communications connection 216 thatallows the device to communicate with other devices including. Suchcommunications connection 216 is an example of communication media.Communication media typically embodies computer-readable instructions,data structures, program modules and/or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, radio frequency (“RF”), infrared and other wireless media. Theterm computer-readable media as used herein includes both storage mediaand communication media.

Computing device 202 may also have input device(s) 214 such as keyboard,mouse, pen, voice input device, touch input device, etc. Outputdevice(s) 212 such as a display, speakers, printer, etc. may also beincluded. All these devices are generally known to the relevant publicand therefore need not be discussed in any detail herein except asprovided.

It should be understood that the various techniques described herein maybe implemented in connection with hardware or software or, asappropriate, with a combination of both Thus, the methods and apparatusof the presently disclosed subject matter, or certain aspects orportions thereof, may take the form of program code (i.e., instructions,scripts, and the like) embodied in tangible media, such as floppydiskettes, CD-ROMs, hard drives, flash drives, DVDs or any othermachine-readable storage medium wherein, when the program code is loadedinto and executed by a machine, such as a computer, the machine becomesan apparatus for practicing the presently disclosed subject matter.

Additionally, input devices 214 may also include devices for monitoring,measuring, and/or recording the various measures that are utilized tocreate a psycho-physiological mood map as described herein including,without limitation, a data acquisition device 102 (FIG. 1) or otherdevices capable of receiving data from fEMG, IRV, GSR, EEG, and otherbehavior monitoring apparatus. Or, data from equipment capable ofmonitoring such measures may be transferred to a memory of computingdevice 202 such as memory 204, removable storage 208, or non-removablestorage 210 using methods known in the art. For example, communicationconnections 212 may include an interface from computing device 202 tosuch equipment Once data regarding the measured and monitored variablesis available to computing device 202, it may run various algorithms toprocess such data into a mood map as described in greater detail herein,Such algorithms may optionally include a weighting algorithm.

In some embodiments of the present invention, a plurality of computingdevices may be incorporated. However, it will be known to one of skillin the art that the functions of a pair of computing devices may becombined into one computing device or greater than two computing deviceswithout departing from the scope hereo.

Turning back to FIG. 5, depicted is one exemplary process 500 forassessing the marketability of a product in accordance with oneembodiment of the present invention. Process 500 begins at 502, at whicha subject who has elected to be tested for the purposes of assessing themarketability of a product is equipped with sensors that will allow thesubject's response(s) to the product, product stimuli, and/or benchmarkstimuli to be recorded by a data acquisition device, a computing device,or the like.

An example of an exemplary system 100 for assessing the marketability ofa product (and equipping a subject for such testing) in accordance withone embodiment of the present invention is depicted in Jig 1. System 100includes, inter alia a data gathering system (i.e., data acquisitiondevice 102, sensors 106, 108, 110, 112, 116, 118, and 120, andtransmitters 114 and 122), computing devices 202 a and 202 b, andcabling 130 and 132. In the depicted embodiment, computing device 202 awould be utilized by the administrator of the method to monitor the databeing measured for the subject of the consumer testing. A secondcomputing device 202 b would be utilized by the subject of the consumertesting to perform functions including, but not limited to, viewingproduct or product stimuli and entering response data

In the embodiment depicted in FIG. 1, the electrophysiological measuresto be recorded include fEMG (for emotional valence), (2) HRV (forattention), and (3) GSR (for arousal) as discussed in greater detailabove. fEMG sensors 106, 108, 110, 112, and 116, GSR sensors 120, andHRV sensors 118 may be located on the face, palm of hand, and forearm,respectively, of the subject in a manner identical or similar to thatshown in FIG. 1.

In one form of the invention, these sensors are in the form of wirelessdisposable and/or reusable electrodes 106, 110, 112, 118, and 120 andgrounds 108 and 116, For example, the fEMG sensors may be 4 millimeter(“mm”) in size, snap, latex-free, vinyl, reusable electrodes used withSigna Gel such as, for example, the 4 mm EL654 Ag—AgCl reusable snapelectrodes as manufactured by Biopac. In the depicted embodiment, thesesensors measure electrical activity from three facial muscle groups: thecorrugator supercilii muscle group (measured via electrode pair 106 aand 106 b); the zygomaticus major muscle group (measured via electrodepair 110 a and 110 b); and the orbicularis oculi muscle group (measuredvia electrode pair 112 a and 112 b)). The corrugator supercilii musclegroup is associated with frowning and negative affect (i.e., negativeemotional valence), whereas the zygomaticus major and orbicularis oculimuscle groups are associated with smiling and positive affect (i.e.,positive emotional valence). Although the depicted embodiment of theinvention includes measuring activity in three facial muscle groups,greater or lesser muscle groups could be substituted, or facial musclegroups could be omitted entirely, without departing from the scope ofthe present invention.

The HRV sensors 118 a, 118 b, and 118 c, for example, may be eleven (11)mm in size, snap, latex-free, vinyl, disposable electrodes used withelectrolyte gel for measuring HRV from the skin of the forearm, Oneexemplary electrode is the EL503 disposable electrode as manufactured byBiopac. HRV may be calculated by measuring the distance between positiveR peaks of the EKG waveform (e.g., those depicted in data acquisitionsoftware such as Biopac's AcqKnowledge software) and as expressed inbeats per minute (BPM). Increases in HRV are indicative of distractionor decrease in attention whereas decreases in HRV are indicative ofincreased attention and focus.

The GSR sensors 120 a and 120 b, for example, may be 16 mm in size,snap, latex-free, vinyl, disposable electrodes used with an isotonic gelfor measuring electrical skin conductance (i.e., electrodermal activity)from the palm of the hand. One exemplary electrode is the EL507disposable electrode as manufactured by Biopac. GSR is measured with aconstant voltage of 0.5 V across the skin. The conductivity deltabetween the enter and exit electrodes is used to calculate microsiemensof conductance. These values are compared to baseline (resting state)measures to indicate changes in arousal. Changes in conductivity thatare increases from baseline are indicative of increase in arousalwhereas decreases in conductivity compared to baseline are indicative ofdecreases in arousal.

In the depicted embodiment of the present invention, the physiologicaldata is collected from the sensors via a plurality of data transmittersthat wirelessly communicate with data acquisition device 102. In oneembodiment of the present invention, data acquisition device 102 is anMP150 Data Acquisition System and the sensors are BioNomadix® devices.

The data acquisition device 102 receives the data from the transmitters114 and 122 wirelessly and communicates the data to one or morecomputing devices for data analysis. In the depicted embodiment, dataacquisition device 102 is hardwired to two computing devices 202 a and202 b via cabling 132 and 130, respectively. In this exemplaryembodiment, computing device 202 a is equipped with data acquisitionsoftware (e.g., AcqKnowledge software as manufactured by Biopac) andother software as described herein with reference to FIG. 5, the latterof which is utilized to convert and analyze the acquired data. The dataacquisition software records data (e.g., waveforms) from a plurality ofchannels of the data acquisition device 102, This data may include,without limitation, EKG, EDA, facial muscle movement, and stimuluspresentation signaling data. The stimulus presentation signaling datamay be transmitted to the data acquisition device 102 by stimuluspresentation software that may, for example, be executed by a computingdevice such as computing device 202 b. The stimulus presentationsignaling would indicate when the stimulus is presented and when suchpresentation ends.

In the depicted embodiment, computing device 202 b is equipped withe-Prime. Cabling 132 and 130 are Ethernet cross-over and parallel portcables, respectively. Also, in the depicted embodiment, the dataacquisition software is AcqKnowledge software as manufactured by Biopac,which records the data received from data acquisition device 102. Thestimulus presentation software provides the subject with displays of theproduct stimulus and/or benchmark stimulus and triggering data relatingto the onset/offset of the product and/or benchmark stimuluspresentation. Although the depicted embodiment includes two (2)computing devices, more or less computing devices may be utilizedwithout departing from the scope hereof. Further, software other thanAcqKnowledge and/or e-Prime may also be substituted so long as it isable to perform the necessary functions.

In the depicted embodiment of the present invention, filter settings maybe set as follows: low-pass and high-pass 500 Hz for fEMG; low-pass 3 Hzand high-pass DC for GSR; and low-pass 35 Hz and high-pass 1 Hz forIRV/EKG. Signals are recorded via the AcqKnowledge software at 1000 Hz.However, alternate settings may be substituted without departing fromthe scope hereof.

Referring back to FIG. 5, after the subject is equipped at step 502, forexample as described above with respect to FIG. 1, method 500 proceedsto 504, at which the subject is exposed to the product and/or benchmarkstimulus and subject product data (if product stimulus is beingpresented) or subject benchmark data (if benchmark stimulus is beingpresented) is recorded. That is, each subject is exposed to the productand/or benchmark stimulus for a predetermined amount of time, which mayvary depending upon the type of product and/or benchmark stimulus, whilethe various data described herein (the “subject product data” or“subject benchmark data”) are recorded (e.g., fEMG, GSR, and HRV).

When the product is a consumer good or service, for example, productstimuli may be 2D or 3D images presented via a computing device such ascomputing device 202 b (FIG. 1), Or, for a consumer good, the productstimulus may be an actual good that is held in the subject's hand(s).When the product is an experience, the product stimulus may includeplacing the subject in the location of the experience. Benchmarkstimulus may be virtually any item that is capable of eliciting anemotion including, without limitation, images, videos, third partyproducts, and competitor products.

The subject's physiological measures are recorded while the subject isexposed to the product and/or benchmark stimulus, for example: while thesubject views the product and/or benchmark stimuli in the form of one ormore images, while the subject holds the product and/or benchmarkstimulus; and/or as the subject experiences some form of product and/orbenchmark stimulus in the form of stimulation (e.g, sensory stimulation,subject interaction with a service provider, subject setting, etc.).Sensory stimulation may include, but is not limited to, lighting, music,fragrance, etc. Subject setting may include, but is not limited to,features of a room the subject is within such as furniture, etc.

The data acquisition software is responsible for presenting the stimulusto the subject, Prior to the recording of data, a stimulus presentationprogram may be programmed including, without limitation, the stimulus tobe presented, the exposure time for each stimulus, and the time lapsebetween presentation of multiple stimuli. In the depicted embodiment,the stimulus presentation software sends stimulus timing signals to dataacquisition device 102 via cabling 130, and the data acquisitionsoftware receives the stimulus timing signals as an input from the dataacquisition device 102 via cabling 132. In the depicted embodiment, inaddition to presenting stimulus (e.g., images, video, websites, etc.) tothe subject, the stimulus presentation software is also capable ofreceiving and recording answers to survey questions.

Additionally, the data acquisition software may be capable of displayingthe raw data and stimulus timing information in real time as the data ismeasured and/or recorded. Additionally, channels may be set up tocalculate values based upon the raw data (e.g., root-mean-square fEMG,heart rate data, etc.) and such values may also be displayed inreal-time.

After the data is collected by the data collection software (e.g., theAcqKnowledge software referenced above), the subject is de-equipped(i.e., all sensors are removed) at 506. If, at 508, additional subjectsare to be tested, process 500 returns to 502 and repeats steps 502-505,Otherwise, process 500 proceeds to 510, at which the subject productdata is cleaned. For example, subject benchmark data may be testedduring the same testing session as subject product data; however,different subjects may be utilized for benchmark testing and producttesting. Or, the same subject may be utilized to test for both benchmarktesting and product testing. Further, in some embodiments, subjectbenchmark data may be tested in one or more separate testing sessionsfrom the product and save for future use.

In the depicted embodiment, data is cleaned for purposes including, butnot limited to, elimination of noise. For example, for the fEMG data,raw waveforms may be normalized using normalization functions within thedata acquisition software. Also, EKG data may be cleaned to eliminatenoise in the EKG channel via, for example, excluding peaks associatedwith amplitudes that are outside the expected range. Also, for the rawfEMG and EKG data, a comb band stop function in the data acquisitionsoftware is run to remove any line frequency (i.e., 60 Hz in the USA)from the waveform.

For HRV, heart rate analysis functions may be applied to the raw EKGdata utilizing the data acquisition software. For example, the dataacquisition software may be used to detect heart beat peaks and todetermine how many occur in a predetermined period of time to determinerate. Also, any HRV data that changes by more than 10 BPM from onesecond to the next is removed by doing a linear interpolation. Forexample, if the BPM data trends at 59, 60, 82, 62, 61, then the value 82is nonlinear and is deemed invalid (and removed). A linear interpolationfrom the previous data point (60) to the following data point (62) willoperate to replace the value of 82 with the value of 61.

For GSR, tonic waveforms may be normalized utilizing the dataacquisition software. Also, if the raw GSR waveform is noisy, a highpass filter of 0.05 hz can be run to eliminate such noise.

Next, process 500 proceeds to 511, at which the data is re-sampled asneeded. In the depicted embodiment of the invention, the data isre-sampled from 1000 Hz to 20 Hz via the averaging of every 50 ms ofdata.

After the data is re-sampled, method 500 proceeds to step 512. At step512, the data is converted into a tabular format for furthermanipulation. An example of such tabular data is shown in table 600 ofFIG. 6. Due to the large volume of data that is taken, table 600 onlydepicts a partial view of the data of a typical session with a subject.

Referring now to table 600 of FIG. 6, Column 602 and 604 includesabsolute time data for the data that was re-sampled at step 511 in msand seconds, respectively, Columns 606, 608, and 610 include there-sampled fEMG data relating to the orbicularis oculi, zygomaticusmajor, and corrugator supercilii muscle groups, respectively. The valueshown in column 610 is raw data multiplied by negative one in order tomake these values a positive number.

Columns 612 and 614 include GSR and HRV data, respectively. Column 615includes data representing the timing of the product stimulus triggers,which may, in the exemplary example, be sent from the stimuluspresentation software or similar software of computing device 202 b todata acquisition device 102.

Referring back to FIG. 5, at step 513, the tabular data is then analyzedand manipulated to create a single data point. In the depictedembodiment, the data to be analyzed is the data below and to the left ofthe dark line 616. This data is the data that was measured duringpresentation of the product or product stimulus to the subject asindicated by the value of five in column 615. The five represents thevalue received by the c-Prime stimulus provision software in response tothe sending of a stimulus trigger to the data acquisition device 102.Therefore, this value indicates that the subject was exposed to thestimulus at the time the data was measured. Alternate values andalternate stimulus provision software may be utilized without departingfrom the scope hereof.

To generate the single subject and/or benchmark data point, first, foreach channel, the baseline is calculated by taking the average of thedata measured during the five (5) seconds preceding presentation of thestimulus to the subject. For example, the baseline value for the Bcolumn data collecting during the subject's exposure to the stimulus(i.e., the data listed in column B and rows 102 and up until the pointthat the stimulus is removed as indicated by the presence of a zero incolumn G) would be calculated by summing the values in cells B2 throughB101 and dividing this value by 100.

Next, change scores are calculated by subtracting the baseline from eachpoint in the exposure data

Change Score=Re-sampled Raw Data Value−Baseline.

A change score is calculated for each re-sampled raw data value, and thechange scores for columns 606-614 are shown in columns 618626,respectively. The baseline remains constant for all data values relatingto a specific presentation of a stimulus to a subject. However, if afirst stimulus is removed and a second stimulus is to be applied to thesubject, a break of at least five (5) seconds will be provided to allowfor the collection of new baseline data to be used in calculation of thechange scores for the second stimulus presentation. For example, in ascenario in which the stimulus is provided via a computing device (e.g,computing device 202 b of FIG. 1), the screen may go black while thedata is collected for the second stimulus baseline. However, alternateembodiments are envisioned in which a new baseline is not calculated(i.e., the prior baseline is utilized) or a lesser amount of timeseparates the presentation of consecutive stimuli presentations.

In continuation of our example above, the Change Score value for thedata recorded in, for example, the B102 cell would be calculated bysubtracting the baseline data as calculated above from the data value inthe B102 cell and this value would then be recorded in the K102 cell.This calculation would be repeated for all re-sampled raw data valuesthat were measured during presentation of a stimulus to a subject.

The singular values that will represent each channel are then calculatedby averaging the change scores associated with the four (4) second timeperiod for the respective channel that occurred directly afterpresentation of the stimulus to the subject. That is, all of the changescores that are associated with the re-sampled raw data of thepredetermined four (4) second time period are averaged to create asingle value. In an embodiment such as the depicted embodiment in whichthe data is re-sampled to 20 Hz, one (1) second equals 20 rows ofre-sampled raw data. Therefore, calculating the average change score forthe four (4) second time period will involve summing the change scoresassociated with that time period (i.e., eighty change scores unless someare discarded) and dividing by the total number of change scores.

Once a single value has been calculated for each channel, the singularvalues that will become the x, y, and z (i.e., the fEMG, GSR, and HRV)coordinates for the Point of Interest (“POI”) or benchmark point in themood map may be calculated, That is, the single subject and benchmarkdata points are the points at which the POI or benchmark point,respectively, is located on the mood map. Therefore, in the depictedembodiment, these single data points are actually a set of x, y, and zcoordinates.

First, the fEMG channel values are manipulated to calculate the value ofthe Valence data._Valence data correlates to the x-coordinate of theexemplary mood map depicted in FIG. 7 as described in greater detailherein.

The Valence data point is calculated by averaging the channel values forthe zygomaticus major (the “ZYG Value”)(i.e., column 608 in table 600 ofFIG. 6) and orbicularis oculi (the “OO Value”)(i.e, column 606 in table600 of FIG. 6) muscle groups (i.e., the muscle groups associated withsmiling and positive affect which represent positive emotional valence)to create a positive valence value.

The channel value for the corrugator supercilii muscle group (the “CORRValue”)(i.e., column 610 in table 600 of FIG. 6) represents the negativevalence value. The absolute Valence value is calculated as follows:

_Absolute Valence value=Positive Valence−Negative Valence

For example, if the ZYG Value is 4, the OO Value is 4, and the CorrValue is 2, the calculation is as follows:

The average of the ZYG and OO Values is 4 (i.e., (4+4)/2)

When the Corr Value is subtracted from the average value, a value of 2is reached (i.e., 4−2), Since 2>0, the overall facial muscle groupreaction is positive.

In another example, if the ZYG Value is 2, the OO Value is 2, and theCorr Value is 4, the calculation is as follows:

The average of the ZYG and OO Values is 2 (i.e., (2+2)/2)

When the Corr Value is subtracted from the average value, a value of −2is reached (i.e., 2−4). Since −2<0, the overall facial muscle groupreaction is negative.

This absolute valence value represents the x coordinate of the POI. Thisis one method of determining an absolute valence value, however,alternate methods may be substituted. Also, in some embodiments of thepresent invention a lesser or greater number of facial muscle groups maybe measured without departing from the scope hereof and the equation maybe modified accordingly. For example, if only one facial muscle group ismeasured or included in the data analysis, the value of the Valencevalue will equal the channel value for that muscle group.

The value of the Attention coordinate may be determined by the followingequation:

Attention=1/x, wherein x is the value of the HRV data channel (i.e.,column 614 in table 600 of FIG. 6). Attention data correlates to thez-coordinate of the exemplary mood map depicted in FIG. 7 as describedin greater detail herein.

Arousal data correlates to the y-coordinate of the exemplary mood mapdepicted in FIG. 7 as described in greater detail herein. The arousalvalue is merely the value of the arousal channel (i.e., column 612 intable 600 of FIG. 6).

After the single subject and/or benchmark data points have beencalculated at step 513, method 500 proceeds to 514, at which the singlesubject data points and single benchmark data points are mapped, in theexemplary depicted embodiment, the single subject data point is mappedonto map 700 as shown in FIG. 7 by mapping the POI 702 for theparticular stimulus presentation on the map in accordance with its x, y,and z coordinates as calculated using methods as described herein. Thatis, the POI is mapped in accordance with the values calculated in step513. The POI value is the biometric data point that represents thesubject's response to the product and/or product stimuli. This mappingis described in further detail herein with regards to FIG. 8B.

Similarly, in the exemplary depicted embodiment, the single benchmarkdata points for each of the benchmark stimuli are mapped onto map 700 asshown in FIG. 7 by mapping the point for the particular benchmarkstimulus on the map in accordance with its x, y, and z coordinates ascalculated using methods as described herein. That is, the point ismapped in accordance with the values calculated in step 513. Thismapping is described in further detail herein with regards to FIG. 8B.

In the depicted embodiment, the method includes mapping twenty biometricsingle benchmark data points 704 onto the mood map, namely, interested,distressed, excited, upset, strong, guilty, scared, hostile,enthusiastic, proud, irritable, alert, ashamed, inspired, nervous,determined, attentive, jittery, active, and afraid. These twenty pointsare taken from the Positive and Negative Affect Schedule (“PANAS”)developed by Watson, Clark, and Tellegen (1988b). The POI 702 iscompared to the benchmark points 704 in order to determine the mostapplicable emotional terms to be assigned to the product being tested.

The location of the benchmark data points 704 in map 700 may bedetermined as described above or using alternate methods withoutdeparting from the scope hereof. In the depicted embodiment, thebenchmark data points were tested via a survey having a plurality ofrespondents. In one embodiment, one hundred and twenty-six (126)respondents participated in the survey Each of the respondents was showntwenty (20) benchmark stimuli. In the depicted embodiment, thesebenchmark stimuli were in the form of images were taken from GAPED,however, alternate benchmark stimuli could be substituted withoutdeparting from the scope hereof. The twenty (20) benchmark stimuli areselected with enough variety that the set of images encompasses themajority of the twenty (20) designated emotional terms.

The GAPED benchmark points have been previously measured viapsychometrics for their valence and arousal values. In the depictedembodiment, to obtain each benchmark's location on the 3D mood map suchas map 700, the survey respondents are equipped in the same manner asthe subjects of the product testing as described herein with respect toFIGS. 1 and 5 and x, y, z coordinates are calculated for the plotting ofbenchmark points in the same manner as they are calculated for theplotting of the POI. That is, in order to calculate the location of abenchmark point on a mood map such as mood map 700, steps 502 through513 are executed as described herein for the POI to determine thebenchmark point's x, y, and z coordinates as described above. In thismanner, benchmark data points 704 and POI 702 are derived in the samemanner except that the benchmark point, through the survey data, hasconnection to emotional terms.

The respondents were then asked to select an emotional term from thelist of twenty (20) emotional terms set forth above to describe thebenchmark stimulus shown to the respondent The percentage of respondentsthat chose the same emotional term for the benchmark stimulus wastallied and utilized as a weighting value. For example, if sixty three(63) of the one hundred and twenty six (126) people picked “Interested”to describe a particular benchmark stimulus, the emotional term ofInterested was assigned a weighting value of fifty (50) percent or 0.5.When determining the distance from a POI to the benchmark point, theweight is used as a multiplier (as described in greater detail herein)to modify the strength of association with the emotional term. That is,the survey provides emotional labeling for these points and theirrelative strength of association.

For example, a benchmark stimulus may include an image of a baby whichhas a highest ranked emotional term of “enthusiastic” because 19% of thesurvey respondents choose this emotional term from the list of twenty(20) possible emotional terms. Such an emotional term would be assigneda weight of 0.19. Also, when the survey respondent was tested via asystem such as that shown in FIG. 1 and the coordinates of the benchmarkpoint were determined using steps similar to those of 502-514 of method500 as shown in FIG. 5, this stimulus is also associated with positivearousal.

Each of the twenty (20) benchmark stimuli has a percentage weighting foreach of the twenty (20) emotional terms, thereby providing four hundred(400) weighting values to be used to modify the distance calculationbetween the benchmark points and the POI(s). There are no exact mood maplocations for the emotional terms, but the closer a POI is to abenchmark point with a high association with a term, the more thatemotional term will be designated to apply to the POI. In the depictedembodiment, top value emotional term(s) may be used to describe aproduct.

Once all single benchmark data points and the single subject data point(“POI”) have been mapped, method 500 proceeds to 516, at which valuesare calculated for all of the emotional terms. For simplification, wewill show the data analysis relative to five (5) benchmark points only,but the analysis is performed for all twenty (20) benchmark points ofthe depicted embodiment. The value of the emotional terms is determinedby first calculating the distance (a-e)(706 a-706 v) from the PO to eachbenchmark point (A-E)(704 a-704 e). Each benchmark point has twenty (20)ranked percentage values for each emotional term, as determined from aseparately conducted random survey. An example weighting is shown belowfor the five exemplary emotional terms:

A B C D E Term 1 40% 10% 20% 10% 10% Term 2 20% 50% 20% 10% 20% Term 320% 10% 20% 10% 40% Term 4 10% 20% 10% 60% 20% Term 5 10% 10% 30% 10%10%

Distance: a b c d e POI to Benchmark: A B C D E Term 1 0.4 0.1 0.2 0.10.1 Term 2 0.2 0.5 0.2 0.1 0.2 Term 3 0.2 0.1 0.2 0.1 0.4 Term 4 0.1 0.20.1 0.6 0.2 Term 5 0.1 0.1 0.3 0.1 0.1

Distance from the POI to the benchmark points can then be weighted bythe percentages shown above thereby providing the followingcalculations:

POI(Term 1: Nervous)=0.4*a+0.1*b+0.2*c+0.1*d+0.1*e

POI(Term 2: Enthusiastic)=0.2*a+0.5*b+0.2*c+0.1*d+0.2*e

POI(Term 1: Interested)=0.2*a+0.1*b+0.2*c+0.1*d+0.4*e

POI(Term 1: Active)=0.1*a+0.2*b+0.1*c+0.6*d+0.2*e

POI(Term 1: Afraid)=0.1*a+0.1*b+0.3*c+0.1*d+0.1*e

The relative applicability of each term for the POI can now bedetermined in the form of an emotional term value to figure out whichemotional term stands out as the most relevant term.

POI (Nervous)=9

POI (Enthusiastic)=12

POI (Interested)=10

POI (Active)=12

POI (Afraid)=7

In this case, emotional terms 2 and 4 have the highest ranking emotionalterm values, therefore, the emotional terms “Enthusiast” and “Active”are most applicable to the POI, The POI associated with the subject'sdata for that particular product stimulus has now been assignedcategorical emotional values based on biometric data alone.

Although the depicted embodiment depicts the mapping of data incomparison to subject benchmark data, alternate embodiments areenvisioned in which the data is analyzed stand-alone, in comparison toadditional test samples, and/or in comparison to competitor products.

Although exemplary embodiments may refer to utilizing aspects of thepresently disclosed subject matter in the context of one or morestand-alone computer systems, the subject matter is not so limited, butrather may be implemented in connection with a multi-computer computingenvironment. Still further, aspects of the presently disclosed subjectmatter may be implemented in or across a plurality of processing chipsor devices, and storage may similarly be affected across a plurality ofcomputing devices 202. Such devices might include personal computers,network servers, and handheld devices (e.g., cell phones, tablets,smartphones, etc.), for example.

Although several processes have been disclosed herein as software, itmay be appreciated by one of skill in the art that the same processes,functions, etc. may be performed via hardware or a combination ofhardware and software. Similarly, although the present invention hasbeen depicted as a hardwired system, these concepts may be applied towireless systems and hybrid hardwired and wireless systems withoutdeparting from the scope of the present invention.

It will be appreciated by those skilled in the art that changes could bemade to the embodiments described above without departing from the broadinventive concept thereof. It is understood, therefore, that thisinvention is not limited to the particular embodiments disclosed, but itis intended to cover modifications within the spirit and scope of thepresent invention as defined by the appended claims.

1. A system for assessing the marketability of a product comprising: atleast one data gathering system for sensing subject product data andsubject benchmark data, at least a portion of the at least one datagathering system equipped to a subject; a computing device having aprocessing unit for receiving the subject product data and the subjectbenchmark data from said at least one data gathering system, theprocessing unit operatively coupled to a non-transitory computerreadable medium, comprising instructions stored thereon, which, whenexecuted by the processing unit, perform the steps of: presenting atleast one benchmark stimulus to the at least one subject; recording thesubject benchmark data; presenting at least one product stimulus to theat least one subject; recording the subject product data; manipulatingthe subject product data to create a single subject data pointrepresenting the location of the subject product data on a 3D mood map;manipulating the subject benchmark data to create at least one singlebenchmark data point, each of the at least one single benchmark datapoints associated with one of the at least one benchmark stimuli, the atleast one single benchmark data point representing the location of arespective one of the at least one benchmark stimuli on the 3D mood map;and calculating at least one emotional term value based upon said singlesubject data point and the at least one single benchmark data point,each of the at least one emotional term value associated with one of atleast one emotional term.
 2. A system according to claim 1, wherein thecalculating at least one emotional term value includes weighting one ormore of the at least one subject benchmark data point.
 3. A systemaccording to claim 1, wherein the processing unit further performs thesteps of: mapping said single subject product data point on the 3D moodmap; and mapping the at least one subject benchmark data point on the 3Dmood map.
 4. A system according to claim 1, wherein the subject productdata and the subject benchmark data include one or morepsycho-physiological measures.
 5. A system according to claim 4, whereinthe psycho-physiological measure is selected from facialelectromyography, heart rate variability, galvanic skin response,electroencephalogram, eye tracking, behavioral analysis, andcombinations thereof.
 6. A system according to claim 5, wherein thefacial electromyography measure measures electrical activity from atleast one facial muscle group selected from a corrugator superciliamuscle group, a zygomaticus major muscle group, an orbicularis oculimuscle group, and combinations thereof.
 7. A system according to claim1, wherein the data gathering system includes one or more sensors, eachof the one or more sensors for measuring a psycho-physiological measure.8. A system according to claim 7, wherein the psycho-physiologicalmeasure is selected from facial electromyography, heart ratevariability, galvanic skin response, electroencephalogram, eye tracking,behavioral analysis, and combinations thereof.
 9. A system according toclaim 8, wherein the facial electromyography measure measures electricalactivity from at least one facial muscle group selected from acorrugator supercilia muscle group, a zygomaticus major muscle group, anorbicularis oculi muscle group, and combinations thereof.
 10. A methodfor assessing the marketability of a product comprising the steps of:equipping at least one subject with at least a portion of a datagathering system for gathering subject product data and subjectbenchmark data; exposing the at least one subject to at least onebenchmark stimulus; recording the subject benchmark data; exposing theat least one subject to a product stimulus; recording the subjectproduct data; manipulating the subject product data to create a singlesubject data point representing the location of the subject data on a 3Dmood map; manipulating the subject benchmark data to create at least onesingle benchmark data point, each of the at least one single benchmarkdata points associated with one of the at least one benchmark stimuli,the at least one single benchmark data point representing the locationof a respective one of the at least one benchmark stimuli on the 3D moodmap; and calculating at least one emotional term value based upon saidsingle subject data point and the at least one single benchmark datapoint, each of the at least one emotional term value associated with oneof the at least one emotional term.
 11. A method according to claim 10,wherein the calculating at least one emotional term value includesweighting one or more of the at least one subject benchmark data point.12. A method according to claim 10, further comprising the step of:mapping said single subject product data point on the 3D mood map;mapping the at least one subject benchmark data point on the 3D moodmap.
 13. A method according to claim 10, wherein the subject productdata and the subject benchmark data include one or morepsycho-physiological measures.
 14. A method according to claim 13,wherein the psycho-physiological measure is selected from the groupconsisting of facial electromyography, heart rate variability, galvanicskin response, electroencephalogram, eye tracking, behavioral analysis,and combinations thereof.
 15. A method according to claim 14, whereinthe facial electromyography measure measures electrical activity from atleast one facial muscle group selected from a corrugator superciliamuscle group, a zygomaticus major muscle group, an orbicularis oculimuscle group, and combinations thereof.
 16. A method according to claim10, wherein the data gathering system includes one or more sensors, eachof the one or more sensors for measuring a psycho-physiological measure.17. A method according to claim 16, wherein the psycho-physiologicalmeasure is selected from facial electromyography, heart ratevariability, galvanic skin response, electroencephalogram, eye tracking,behavioral analysis, and combinations thereof.
 18. A method according toclaim 17, wherein the facial electromyography measure measureselectrical activity from at least one facial muscle group selected froma corrugator supercilia muscle group, a zygomaticus major muscle group,an orbicularis oculi muscle group, and combinations thereof.
 19. Anon-transitory computer-readable medium for assessing the marketabilityof a product, comprising instructions stored thereon, that when executedon a processor, perform the steps of: presenting at least one benchmarkstimulus to the at least one subject; recording the subject benchmarkdata; presenting at least one product stimulus to the at least onesubject; recording the subject product data; manipulating the subjectproduct data to create a single subject data point representing thelocation of the subject product data on a 3D mood map; manipulating thesubject benchmark data to create at least one single benchmark datapoint, each of the at least one single benchmark data points associatedwith one of the at least one benchmark stimuli, the at least one singlebenchmark data point representing the location of a respective one ofthe at least one benchmark stimuli on the 3D mood map; and calculatingat least one emotional term value based upon said single subject datapoint and the at least one single benchmark data point, each of the atleast one emotional term value associated with one of the at least oneemotional term.
 20. A method according to claim 19, wherein thecalculating at least one emotional term value includes weighting one ormore of the at least one subject benchmark data point.